In an orthogonal frequency division multiplexed (OFDM) transmission system an available frequency band is divided into multiple smaller frequency bands. Multiple signals are then modulated and simultaneously transmitted on these multiple frequency bands.
Possible modulation schemes used with OFDM include binary phase shift keying (BPSK), in which one bit is encoded to each symbol, quadrature phase shift keying (QPSK), in which two bits are encoded to each symbol, or even a quadrature amplitude modulation (QAM) scheme in which multiple bits are encoded to each symbol.
In order to simplify the design of transceivers, symbol modulation in an OFDM device is often performed in the frequency domain. Then, prior to transmission, an inverse discrete Fourier transform (IDFT) is performed on the signal to move it into the time domain, where it can be transmitted as appropriate radio frequency (RF) signals. Likewise, when a transceiver receives signals in the time domain, it performs a discrete Fourier transform (DFT) on the signals to move the signal back into the frequency domain for symbol demodulation and data extraction.
Ideally, the signal received by a receiver device will be the same in amplitude as the signal transmitted by a transmitter device. However, in any kind of fading channel, such as a wireless transmission channel, the amplitude of a received signal will vary based on the particular properties of the channel. For example, signal interference can reduce the power of a received signal, while multipath reflections can increase the power of the received signal.
In many OFDM systems, therefore, the receiver will perform a channel estimation process to determine the effect that the channel has on a received signal. Based on this channel estimation, the receiver can then determine how to compensate the received signal for channel fading in order to retrieve the originally-transmitted signal.
One way this can be accomplished is if the receiver knows at least part of the received signal ahead of time. Unfortunately, transmitted data is typically unpredictable, so it can't be used for this purpose. However, one solution is to embed a known symbol pattern into the transmitted signal in place of some data. By examining the effect of the channel on the known portion of the signal, the receiver can estimate the effect of the channel on the entire signal, allowing it to determine how to compensate for the channel effect.
Some channel estimation circuits can include DFTs and IDFTs. But the circuit design for many DFTs and IDFTs can be comparatively complicated and expensive, making the design of the channel estimation circuit containing such a DFT or IDFT likewise complicated and expensive. One type of DFT and IDFT that is relatively simple, however, is a fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) used for powers of two. The expense and complication of the DFT and IDFT circuits can be limited by using FFT and IFFT circuits for powers of two.
But requiring the use of an FFT or IFFT either limits the choice of a known signal portion to signal portions whose lengths are powers of two, which may unduly limit their design, or requires that signal samples be passed through an FFT and IFFT despite the fact that they do not have the proper number of samples. Also in general, performing FFT/IFFT on blocks of non periodic data causes “edge effect” also known as “Gibbs phenomenon” this can cause significant disruption of the accuracy of the channel estimation.
Therefore, it would be desirable to provide a method and circuit for channel estimation that improves the accuracy of channel estimation by, for example, reducing edge effects.